Mapping of Conjunctive Water Use Productivity Pattern in an Irrigation Command Using Temporal IRS WiFS Data

Irrigated agriculture in many areas of the world is currently being practiced from multiple water sources such as precipitation, canal, wetlands, ground aquifer, etc. This study highlights the use of high temporal remote sensing data [IRS-1D; Wide Field Sensor (WiFS), 188-m resolution] to assess conjunctive water use pattern and its productivity in the 6 Main Canal command of Damodar Irrigation Project West Bengal, India. In this command three sources of water (canal water, groundwater and wetland) were used for the rice growing system during the summer season. A multi-date (ten dates, two bands) image stack was prepared. Using this image stack and an unsupervised classification (Fuzzy k-means) backed by space-time spiral curve (ST-SCs) technique, canal release and wetlands information was used to prepare irrigated classes (canal, groundwater or wetlands) map for summer 2000. ST-SCs have been used to analyze temporal WiFS data to continuously monitor class dynamics over time and space and to determine class separability (different types of irrigated-classes) at various time periods within the season. Results showed that the area under agriculture, non-agriculture and water were 81%, 18.5% and 0.5%, of the total area respectively. While, groundwater, canal water and wetland irrigated rice were 67.6%, 25.6% and 6.8%, respectively out of the total agriculture area. Classification results found to have more than 89.3% overall accuracy for broad land cover, while sub-classes of rice i.e. irrigated classes found have reasonably good accuracy of 85.7%. A productivity index (LAI/water-requirement) was also developed. Productivity index was high for the wetland and groundwater irrigated rice as compared to the rice irrigated through canal water. These results were weighed against the observed yield data.